Noise-Robust Processing of Phase Dislocations using Combined Unwrapping and Sparse Inpainting with Dictionary Learning

The problem of phase unwrapping from a noisy and also incomplete wrapped phase map arises in many optics and image processing applications. In this work, we propose a noise-robust approach for processing regional phase dislocations. Our approach combines phase unwrapping and sparse-based inpainting with dictionary learning to recover the continuous phase map. The method is validated both using numerically simulated data with strong additive white Gaussian noise and phase dislocations; and experimental data from fringe projection profilometry. Comparisons with other phase inpainting method referred to as PULSI+INTERP, show the suitability of the proposed method for phase restoration even in extremely noisy phases. The error given by the proposed method on the highest level of noise (RMSE=0.0269 Rad) remains the smallest compared to the error given by PULSI+INTERP for noise-free data (RMSE=0.0332 Rad). © 2019 IEEE.

• Fecha de publicación: 2019
• Institución: Universidad Tecnológica de Bolívar
• Repositorio: Repositorio Institucional UTB
• Idioma: eng
• OAI Identifier: oai:repositorio.utb.edu.co:20.500.12585/9155
• Acceso en línea: https://hdl.handle.net/20.500.12585/9155
• Palabra clave: 3-D Reconstruction; Dictionary Learning; Image restoration; Phase unwrapping; Sparse representation; Gaussian noise (electronic); Optical data processing; Restoration; Vision; White noise; 3D reconstruction; Additive White Gaussian noise; Dictionary learning; Fringe projection profilometry; Image processing applications; Phase unwrapping; Sparse representation; Wrapped phase map; Image reconstruction
• Rights: restrictedAccess
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/